Vineyard detection from unmanned aerial systems images

Individual plant definition and missing plant characterization in vineyards from high-resolution UAV imagery

“…The detection of vine rows in an aerial image of a vineyard is carried out using the method proposed in Comba et al (2015). Firstly, through a dynamic-windows segmentation procedure, a binary image is produced in which clusters of interconnected pixels mainly represent vine rows.…”

“…In these systems, despite the high spatial resolution of the sensors currently employed, the outcoming information, such as the vigour zoning, only accounts for averaged data neglecting the contribution of single vine (Arnó, Martínez Casasnovas, Ribes Dasi, & Rosell, 2009). While row detection techniques saw a great development in these last few years (Comba, Gay, Primicerio, & Aimonino, 2015;Delenne, Durrieu, Rabatel, & Deshayes, 2010;Puletti, Perria, & Storchi, 2014;Smit, Sithole, & Strever, 2010), a methodology for single plant detection is still not available. Instead, the ability to recognize automatically single vine within a training row could remarkably improve the representation of the contribution of single plant to the canopy curtain, enabling to detect specific plant pathologies in the row and improving the accuracy of vigour zoning (Lee et al, 2010;Naidu, Perry, Pierce, & Mekuria, 2009;Sankaran, Mishra, Ehsani, & Davis, 2010).…”

“…This is usually due to the premature death of a vine, and apart from a consequent lowering in the vineyard production, it poses some serious problem from a remote sensing point of view. In fact, not only it can be an obstacle to vine row recognition and thus to the automatic processing of aerial map (Comba et al, 2015;Rabatel, Delenne, & Deshayes, 2008) but it can also induce errors in the estimation of vigour zones inside vineyards, and affect the results of the application of precision agriculture techniques in vineyard management.…”

“…The paper is organized as follows: "Materials and methods" presents the proposed approach and the experimental site. The vine rows extraction step is here only briefly exposed since it has been proposed in Comba et al (2015). The following steps of segmentation, parameters calculation and model construction are instead described in detail, as they constitute a new contribution.…”

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

“…In particular, apart from the within-field crop analysis and the estimation of the spatial variability in wine-grape composition and yield [17][18][19][20][21][22][23], there is plenty of research towards the classification, identification and delineation of crops in remote sensing data [24][25][26][27]. However, despite recent research efforts towards the detection and delineation on medium [28][29][30] and high resolution [31,32] spatial scales, the development and validation of efficient classification frameworks for operational vineyard detection in high resolution data and over large agricultural regions still remain a challenge.…”